Using simulation modeling to inform intervention and implementation selection in a rapid stakeholder-engaged hybrid effectiveness-implementation randomized trial

Background Implementation research generally assumes established evidence-based practices and prior piloting of implementation strategies, which may not be feasible during a public health emergency. We describe the use of a simulation model of the effectiveness of COVID-19 mitigation strategies to inform a stakeholder-engaged process of rapidly designing a tailored intervention and implementation strategy for individuals with serious mental illness (SMI) and intellectual/developmental disabilities (ID/DD) in group homes in a hybrid effectiveness-implementation randomized trial. Methods We used a validated dynamic microsimulation model of COVID-19 transmission and disease in late 2020/early 2021 to determine the most effective strategies to mitigate infections among Massachusetts group home staff and residents. Model inputs were informed by data from stakeholders, public records, and published literature. We assessed different prevention strategies, iterated over time with input from multidisciplinary stakeholders and pandemic evolution, including varying symptom screening, testing frequency, isolation, contact-time, use of personal protective equipment, and vaccination. Model outcomes included new infections in group home residents, new infections in group home staff, and resident hospital days. Sensitivity analyses were performed to account for parameter uncertainty. Results of the simulations informed a stakeholder-engaged process to select components of a tailored best practice intervention and implementation strategy. Results The largest projected decrease in infections was with initial vaccination, with minimal benefit for additional routine testing. The initial level of actual vaccination in the group homes was estimated to reduce resident infections by 72.4% and staff infections by 55.9% over the 90-day time horizon. Increasing resident and staff vaccination uptake to a target goal of 90% further decreased resident infections by 45.2% and staff infections by 51.3%. Subsequent simulated removal of masking led to a 6.5% increase in infections among residents and 3.2% among staff. The simulation model results were presented to multidisciplinary stakeholders and policymakers to inform the “Tailored Best Practice” package for the hybrid effectiveness-implementation trial. Conclusions Vaccination and decreasing vaccine hesitancy among staff were predicted to have the greatest impact in mitigating COVID-19 risk in vulnerable populations of group home residents and staff. Simulation modeling was effective in rapidly informing the selection of the prevention and implementation strategy in a hybrid effectiveness-implementation trial. Future implementation may benefit from this approach when rapid deployment is necessary in the absence of data on tailored interventions. Trial registration ClinicalTrials.gov NCT04726371 Supplementary Information The online version contains supplementary material available at 10.1186/s43058-024-00593-w.

Contributions to the literature -How can simulation modeling be used to accelerate effectiveness-implementation trials in vulnerable health disparity populations when data on tailored interventions and implementation strategies are limited and the context is urgent and rapidly-evolving?-A validated microsimulation model of COVID-19 was used to model the potential benefits of mitigation strategies in group homes for individuals with serious mental illness and intellectual/developmental disabilities.-Model results directly informed a stakeholder-engaged process of selecting key components of a tailored bestpractice intervention and implementation strategy in a rapid hybrid effectiveness-implementation trial.-Simulation modeling was successfully used to inform a rapid effectiveness-implementation trial and has potential to benefit future trial designs.

Background
A fundamental premise of implementation science is the overarching goal to "bridge the science to service gap" bringing research to clinical action [1][2][3].Hybrid effectiveness-implementation research designs may help accelerate the pace of doing so [4,5], along with stakeholder-engaged tailoring of interventions to improve fit, uptake and scalability [6][7][8][9].Yet, conventional implementation research designs are difficult when the evidencebase is uncertain or rapidly evolving.Additionally, it is challenging to assess the potential merit of a proposed implementation strategy in the context of an urgent public health imperative when time does not allow for pilot studies of feasibility or effectiveness.Methods are needed to select and tailor prevention and implementation strategies.Simulation modeling allows for the ability to inform decisions even when evidence is less robust than would be ideal, by allowing for the examination of assumptions and the impact of uncertainty on the conclusions.The purpose of this report is to describe the use of simulation modeling to rapidly estimate the potential effectiveness of implementing different preventive practices in the context of a public health emergency in a stakeholder-engaged process of tailoring interventions in a randomized effectiveness-implementation trial.
Responding to the need to simultaneously implement and study the effectiveness of different COVID-19 mitigation strategies, we deployed a validated microsimulation model of COVID-19 transmission to identify "tailored best practices" for the population of residents and staff in group homes for adults with SMI and ID/DD.This microsimulation model was used to produce specific estimates of the impact of different approaches to COVID-19 risk mitigation to inform a stakeholder-engaged process of selecting and tailoring the final intervention and implementation process for a hybrid effectiveness-implementation trial.A comprehensive description of the hybrid effectiveness implementation study protocol is reported elsewhere [39].

Overview
The primary aim of the parent randomized trial is to evaluate the effectiveness and implementation of General model results were presented to multidisciplinary stakeholders and policymakers to inform the "Tailored Best Practice" package for the hybrid effectiveness-implementation trial.

Conclusions
Vaccination and decreasing vaccine hesitancy among staff were predicted to have the greatest impact in mitigating COVID-19 risk in vulnerable populations of group home residents and staff.Simulation modeling was effective in rapidly informing the selection of the prevention and implementation strategy in a hybrid effectiveness-implementation trial.Future implementation may benefit from this approach when rapid deployment is necessary in the absence of data on tailored interventions.

Trial registration ClinicalTrials.gov NCT04726371
Best Practices (GBP) compared to Tailored Best Practices (TBP) in preventing COVID-19 in the staff and residents of group homes for adults with serious mental illness (SMI) and intellectual and developmental disabilities (ID/DD) [39].General Best Practices (GBP) reflected general guidelines for COVID-19 prevention provided by the Massachusetts Department of Health and Human Services and the Center for Disease Control.Tailored Best Practices (TBP) consisted of COVID-19 prevention practices specifically selected to address the risk factors, group home settings, and populations of residents with SMI and ID/DD.The process of selecting the core components of TBP was a collaborative co-design process with stakeholders including SMI and ID/DD residents, group home providers, community service organization representatives and leaders, and advocacy groups.The multidisciplinary, stakeholder-driven process was informed by a validated mathematical simulation model of COVID-19 disease, the Clinical and Economic Analysis of COVID-19 interventions (CEACOV) model, to project the clinical impact of a variety of different potential COVID-19 mitigation strategies in the staff and residents with SMI and ID/DD in group homes.
The CEACOV model is a dynamic microsimulation model of the natural history of COVID-19 disease and utilizes a susceptible, exposed, infectious, recovered (SEIR) framework [40,41].The model is publicly available, as detailed in the Supplement.In this application of the model, three defined population groups were assessed including residents of GHs, staff of GHs, and the surrounding community mixed in a heterogeneous manner.New cases were transmitted at a probability resulting from a combination of the virus infectivity rate, likelihood of contacting others both within and outside the population group, number of other individuals contacted, and length of contact time.Additional details of the model design and structure are published elsewhere and included in the Supplement [40,41].
The model inputs for this analysis were derived to mirror the population of interest for the anticipated hybrid effectiveness-implementation trial consisting of the residents and staff of 415 GHs for individuals with SMI and ID/DD in Massachusetts.The model inputs reflect the population in late February-early March 2021 during trial planning; given the rapidly-evolving nature of the COVID-19 pandemic at the time, the time horizon for model analysis was 90 days.Outcomes included number of new COVID-19 infections in GH residents, number of new COVID-19 infections in GH staff, and total number of hospital-days among GH residents.The model has been validated against Massachusetts data and in similar congregate living settings [40,42].To do so, the model was calibrated to transmission data spanning various 30-day intervals and used to predict the outcomes over the following 15 days.We used the reported number of COVID-19-related deaths as the primary calibration target.To assess model fit, we used the mean absolute percentage error (MAPE) and the median absolute percentage error (MEDAPE) for modeled and observed number of deaths over the validation horizon [42].
The study was approved by the Institutional Review Boards of MassGeneral Brigham, the Massachusetts Department of Developmental Services, and the Massachusetts Department of Mental Health.A review of our stakeholder groups, data source types, and model input derivations follow; additional details of each can be found in the Supplement.

Stakeholder groups
As described in the detailed study protocol [39], projectbased partnerships with stakeholders were instrumental in determining and obtaining model inputs and designing the strategies examined in the model analyses.Figure 1 highlights the various stakeholders that were included in designing and carrying out the model analyses.Additional details of the role of each stakeholder group to the modeling analysis can be found in the Supplement (eTable 1).

Data sources
Data to inform model inputs were derived from four source types: qualitative data, including the design and use of a survey and structured qualitative interviews, obtained from GH residents and staff in partnership with stakeholders; quantitative data collected in collaboration with the GH research team and administration; publicly available data from the Massachusetts Department of Public Health; and published literature.Additional details about how these data sources relate to input parameters can be found in the Data Sources section of the Supplement (Supplement page 2).The entire structure of the model, for further clarification along with the data sources above, is available online at https:// github.com/ MGH-MPEC/ CEACOV.

Input parameters
Model input parameters in the simulation reflected the unique trial GH population, including residents and staff (Table 1).

Cohort
Using baseline data collected from the GHs, including the total populations of 3,469 residents and 4,797 staff across all GHs, an average GH size of 7 residents, 3 staff shifts per day, and average staff:resident ratios, we modeled the cohort of residents and staff at all 415 participating GH sites, along with an estimated 100,000 individuals in the surrounding community.Given that staff sometimes worked at more than one GH, and that residents often shared day programming with residents of other GHs, we modeled all participants together.To account for potential stochastic variation in a relatively small model simulation cohort size, we used model runs with 10 times the number of total individuals across the GHs and scaled results down by a factor of 10 to adjust for the study cohort size.Cohort characteristics, such as gender and age, reflected data gathered from the GH organizations on GH residents and staff; community demographic data were based on publicly-available data.The proportion susceptible to COVID-19 infection was calculated using publicly-available data from Massachusetts in late February-early March 2021, including the total number of active cases, total cases, total deaths, and total population across the Commonwealth.In an effort to generate conservative estimates prior to understanding the full extent of the pandemic on specific population subgroups, the initial proportion susceptible was assumed to be the same across the three population groups.

Contact-hours and infectivity
To understand the dynamics and interactions of residents and staff within and outside the GHs, we used data collected by survey and stakeholder interview to estimate the number of contact-hours for each transmission group in the first year of the pandemic.The force of infection from each transmission group was derived utilizing an overall infectivity per contact-hour weighted by contacthours spent with each other transmission group, capturing the heterogeneity of viral transmission across the population [41,46,50].Since the horizon of the analysis was 90 days, we did not model waning immunity or reinfections.

Mitigation interventions
Mitigation interventions, including COVID testing frequency, isolation, and resident mask adherence, were modeled to reflect actual practice in the GH organizations in the planned trial as determined through survey data.Modeling of mask efficacy, staff and community mask adherence, and staff and community isolation were informed by prior studies [41,47,51].The impact of these interventions on force of infection in the model is described further in the Input Derivation section of the Supplement.

Strategies
An ongoing challenge for the intervention and implementation process to prevent COVID-19 in the group homes was an evolving population incidence rate of COVID-19 and changing evidence base and recommendations for effective preventive practices.Evolving changes in this outer context required ongoing dynamic adaptations and revisions in the components of the intervention and implementation strategies consistent with agile implementation methods and dynamic sustainability.We revised our examined prevention strategies over time to reflect the rapid progression of the COVID-19 pandemic, new knowledge on prevention, and real-time input from stakeholders around shifting abilities of the GHs to support mitigation measures.In the initial early planning stages, preventive interventions included different frequencies of routine testing, off-site isolation of quarantining residents, and staffing changes.However, in discussions with stakeholders, it was determined that these administrative changes were not feasible to be evaluated in a randomized trial.As such, they were not included in the final examined prevention strategies.At the same time, the development and approval of vaccinations in the United States during the planning stages of the implementation trial required a course shift and rapid incorporation of vaccination into the model that was not present as an option in the early planning stages of the study [48].
The final, base case model prevention strategies in the simulation, which were used to help guide decisionmaking of stakeholders in selecting tailored best practice interventions for the trial, consisted of the following: This combination of strategies allowed for assessment of the projected impact of individual prevention measures, layering from one strategy to the next and including those already put in place, to help stakeholders decide which particular prevention measures would be most feasible and impactful to include in the TBP package for the hybrid effectiveness-implementation trial.Quantifying the estimated benefit of specific measures already in place provided stakeholders with additional insight into whether to emphasize such strategies moving forward as part of the TBP package.
Given the large impact of vaccination on reducing infection rates, vaccination uptake was subsequently varied incrementally among residents and staff, both separately and together.Vaccination was also examined with regard to the hiring of new, unvaccinated staff (assumed 25% staff turnover per year in discussion with stakeholders); scale-up from biweekly to weekly asymptomatic testing in GH staff and from symptomatic-only to weekly asymptomatic testing in GH residents; and mask removal.

Outcomes
The primary outcomes of the modeling analysis were new infections in GH residents and new infections in GH staff.The secondary outcome was resident hospital days.Multiple sensitivity analyses were performed, including varying vaccine efficacy in preventing any infection and vaccine efficacy in preventing symptomatic infection.

Planned translation of results to the trial
At the project outset, the process for identifying the intervention components of "Tailored Best Practices" was to use a stakeholder-engaged approach with the plan to transmit the plans for the simulation to stakeholders as part of a stakeholder summit for the trial, the COVID Quality Improvement Collaborative (CQIC) Summit [39].Results were subsequently presented to stakeholders, including the project's research team, data analyst, and GH provider organization leadership, on both a scheduled and ad hoc basis.

Base case prevention strategies prior to vaccine availability
The base case Early Pandemic prevention strategy resulted in 983 new infections in residents and 2,588 new infections in staff over a 3-month period, along with a projected 72 resident hospital-days (Table 2).The Early Pandemic with Masks prevention strategy incrementally reduced resident infections by 52.1% to 472, staff infections by 44.7% to 1,430, and resident hospital-days by 40.3% to 43 over the 3-month period.The Pre-vaccination Status Quo prevention strategy led to

Additional testing and vaccination strategies
We then examined prevention strategies with increased testing frequency, staff turnover, and vaccination uptake (Table 3).Adding weekly staff and resident COVID-19 testing to the Vaccination Current Levels prevention strategy was projected to have minimal impact on resident and staff infections, with only a 1.2% drop in each; this strategy was associated with a 27.8% drop in resident hospital-days, likely attributable to the impact of identification and subsequent isolation linked to an asymptomatic positive test result among residents.At the Vaccination Current Levels, adding in staff turnover resulted in a 4.8% increase in resident infections, a 2.3% increase in staff infections, and a 16.7% increase in resident hospital-days.
We then examined the impact of increasing vaccination uptake among residents alone, staff alone, or residents and staff together (Table 3, Fig. 2).Increasing staff vaccination uptake had the largest impact on decreasing both resident and staff infections over the 3-month time horizon, while increasing resident vaccination uptake alone had less impact.When incrementally removing mask use among the population of residents and staff with 90% vaccination uptake, resident and staff infections increased slightly.Results were robust to sensitivity analyses examining vaccine efficacy in preventing any infection and vaccination uptake in residents, staff, and community members (Fig. 3).

Translation of results
The modeling team presented simulation plans to GH residents, staff, family members, and administrative leadership along with the full research and implementation trial teams at the CQIC Summit in February 2021.At this Summit, the broader study team presented to all stakeholders both qualitative and quantitative data derived from the GHs in the study sample, which ultimately informed the modeling inputs, including demographics, early infection data, precaution measures taken, and perceived impact of the pandemic on daily function.Over the subsequent months in scheduled and ad hoc meetings, stakeholders were provided an overview of the simulated impact and estimated additional benefits of the different prevention measures and weighed the comparative benefits of different measures relative to the practical feasibility of implementing the intervention components into the trial.The modeling results thus directly informed the selection of the key components of the TBP intervention incorporated into the implementation trial.For example, the relatively high resource need associated with routine testing was considered to be outweighed by the benefits of mask use and vaccination.Similarly, despite the improved outcomes predicted with use of masks, it was recognized that this was highly feasible and enforceable for staff, but challenging for individuals with SMI and ID/DD as a mandate in their living environment.Simulation modeling demonstrating a small incremental benefit of pursuing adherence to wearing masks for residents with SMI and ID/DD beyond vaccination confirmed for the stakeholders that the substantial effort required to reinforce this behavior for residents would be better dedicated to behavioral change efforts promoting vaccination.Implementation strategies then prioritized immunization for residents and staff, while at the same time promoting vaccinations and strict adherence to mask use by staff.Model results highlighting the expected impact of vaccine uptake on infections in the GHs were also used in the educational materials used with GH residents and staff as a component of the TBP intervention (Supplement eFigure 1).The simulation modeling results were also presented directly to governmental policymakers, including stakeholders from the Massachusetts Department of Mental Health and Massachusetts Department of Developmental Services.The results of the simulation modeling, particularly around the impact of vaccination for the GH population, were used to advocate for prioritizing GH residents and staff in the statewide vaccine distribution at the time of limited and restricted initial vaccine availability.

Discussion
This study demonstrated the feasibility and potential benefits of applying methods of simulation modeling coupled with a stakeholder-engaged process to rapidly select multi-component, tailored, targeted, intervention and implementation strategies in a hybrid effectiveness-implementation research trial.This approach was developed and applied in the context of a public health emergency in which the incidence of infection and the evidence base for effective preventive practices was evolving, and the target population consisted of a health disparity population at high-risk for adverse outcomes with unique vulnerabilities and capacities.
In the specific context of developing and evaluating best practices to prevent COVID-19 infection in the staff and residents of group homes, our modeling results demonstrated the positive impact of mitigation measures already in place in the GHs and showed that increasing vaccine uptake would be the most efficacious next target for the trial.We found the incremental benefit of mask use in GHs was substantially diminished in the context of effective vaccination, which was of particular significance in the population of adults with SMI and ID/DD in GHs, as efforts to achieve and sustain full compliance with the use of face masks and social distancing was challenging, and in many instances, not possible.In contrast, focusing efforts on implementation strategies aimed at improving resident and, particularly, staff vaccination uptake was shown in simulation to have the maximum potential benefit for both residents and staff.Identifying this focus as Projected cumulative infections in the setting of vaccination levels current at the time of the model analysis are displayed, as well as strategies with staff turnover and increased resident and staff vaccination uptake.Given the lower baseline vaccine uptake among staff, increasing staff uptake makes the largest incremental difference in preventing both resident and staff infections an implementation target was underscored by the lower proportion of staff, compared to residents, who were vaccinated at baseline.While there is little, if any, literature on successful COVID-19 mitigation in GH settings for vulnerable adults with SMI and ID/DD, this finding is similar to that found in a modeling analysis examining mitigation practices in nursing homes, a setting with residential and staff-support functions similar to those in GHs [52].
Conventional implementation research usually relies on the existence of a robust evidence base with the primary goal of identifying an optimal strategy for implementing well-documented, effective practices.In contrast, hybrid effectiveness-implementation trials simultaneously seek to identify the most effective interventions and optimal implementation strategies.In this instance investigators develop hypotheses by the potential components and benefits of a given practice based on preliminary pilot data (if these exist) or extrapolating and adapting related data.In the COVID-19 pandemic, public health officials and researchers were challenged to quickly develop and implement recommendations for special high-risk populations in the absence of precedent.Our investigation demonstrates the value of using microsimulation modeling to inform the prioritization and selection of interventions and implementation strategies in settings serving vulnerable populations for rapid deployment in a public health emergency.Moreover, simulation modeling offers a way to estimate the effectiveness of interventions under novel conditions.As such, we propose that modeling may be useful to inform implementation and hybrid effectiveness-implementation trials even beyond the scope of an emergency and when a greater evidence base is available, to help stakeholders better estimate anticipated costs and benefits and thus prioritize among potential interventions for a trial in a more structured, reliable way.
A limited existing literature supports the use of mathematical simulation modeling to inform the design of research trials, albeit primarily clinical trials [53][54][55][56][57].While recent literature has highlighted the potential value of using simulation modeling in the design of implementation trials [58][59][60], there is a lack of data demonstrating the feasibility and actual use of simulation modeling in the design of implementation research trials, especially for the design of hybrid effectiveness-implementation trials.Sheldrick et al. recently suggested guidelines to inform the use of simulation modeling in implementation -a process they named rapid-cycle systems modeling (RCSM) [60].While our team began this project prior to the publication of the RCSM framework, our goals and process corresponded to that recommended in RCSM, including determining stakeholders' needs, refining a simulation model, and utilizing stakeholder input to iterate modeling [60].Huang and colleagues also recently describe the use of agent-based and microsimulation models to inform implementation trials [59].Our work builds on that analysis by adding networking and incorporating a disease transmission probability, allowing us to examine the spread of an infectious disease in the population.Moreover, Huang et al. detail the process of developing a new model, which can take substantial additional time and resources when designing an implementation trial [59].In contrast, our study benefited from expansion of an existing, validated simulation model, which allowed both for accuracy and rapidity of iterations in the context of the fast-moving pandemic [40][41][42][61][62][63].
Taken together, our analysis and these studies support the feasibility and potential major benefits to utilizing simulation modeling in implementation trial design.We suggest several practices for such future use.First, simulation modeling partnering with stakeholders allows for the use of stakeholder-specific data to project anticipated results for the population and setting of interest.Given that simulation models often rely primarily on the published literature for data, model results may be limited in their generalizability.Alternatively, by partnering with stakeholders directly and collecting data from the source that an implementation study is targeting, modelers can devise and obtain the input parameters needed and address the stakeholders' questions of interest for that specific population.Second, our study demonstrates the use of simulation modeling coupled with stakeholderengaged co-design to inform the identification of the most promising and feasible prevention strategies for implementation.Utilizing this process in future implementation studies may allow for more efficient resource use in implementation research trials, by better narrowing in on the key components of interventions expected to have effective results prior to launching large-scale trials.Third, the results of stakeholder-informed simulation modeling can be used to communicate outcome data in real-time to stakeholders.In this project, modeling results were used in educational materials as part of the tailored best practice intervention; GH staff, residents, and administration were able to learn insights directly from their own data, which helped with buy-in and subsequent tailoring of interventions.Fourth, the process of revising and refining the selected prevention strategy in collaboration with stakeholders to make real-time changes to simulation model strategies and data inputs allowed for refined recommendations for our population of interest.Future implementation studies can also benefit from rapidly-iterated, data-informed simulation model outcomes, particularly in rapidly-changing contexts, exemplified in this study in the rapidly-evolving COVID-19 pandemic.Finally, given that the Food and Drug Administration and other similar agencies look for evidence on plausible mechanisms of action in reviewing applications, simulation modeling of implementation science trials could be used as a part of that process [64].
This study has several limitations.First, given the goal of informing an implementation trial in Massachusetts, the modeling results may not have been generalizable to other settings.However, we believe that the GH-specific data, which we obtained by partnering with stakeholders, may be informative to similar facilities for individuals with SMI and ID/DD across the country, given the paucity of data for this population in the published literature.
Second, we scaled up the number of participants and scaled down our results accordingly in order to account for potential statistical noise in the model results with a small sample size; however, in doing so, some transmission dynamics could have been changed and the structural details of particular GHs may not have been fully accounted for.However, the participation of the research and GH administrative staff allowed for direct communication with the implementation teams about the modeling methodology and interpretation of the results.

Conclusion
We used a mathematical simulation model of COVID-19 disease to rapidly estimate the potential effectiveness of implementing different preventive practices in GHs, in response to a public health emergency and to inform a randomized effectiveness-implementation trial.The results of the simulation modeling confirmed the primary importance of prioritizing vaccination combined with masking for staff in reducing COVID-19 infections in GH settings.In addition, these simulations provided critical data to guide the tailoring of the final intervention and implementation process to the unique context of group homes and the health disparity population of residents with SMI and ID/DD.We also established the successful use of a modeling-implementation partnership to optimize the implementation trial design by using simulation modeling to inform the stakeholder-engaged process of selecting the key components of the intervention and implementation strategies.This paradigm of pilot modeling can accelerate the process and improve the efficiency and quality of future implementation research trials, including in a future public health emergency.

Fig. 1
Fig. 1 Stakeholders for simulation modeling to inform an implementation trial of best practices to mitigate COVID-19 disease in group homes for individuals with serious mental illness and intellectual disability/developmental disability in Massachusetts.This figure depicts the multiple stakeholder groups with whom the simulation modeling team collaborated in order to derive model inputs reflective of the population of interest, namely the residents and staff of a sample of 415 group homes in Massachusetts

Fig.
Fig. new over projection, grouped by vaccination group.This figure displays the projected cumulative number of new COVID-19 infections among residents and staff of 415 group homes for individuals with serious mental illness and intellectual disability/ developmental disability in Massachusetts under a variety of modeled vaccination strategies, over the 90-day modeled time horizon.Model input parameters are informed by data from the group homes, as well as Massachusetts publicly-available public health data and the published literature.Projected cumulative infections in the setting of vaccination levels current at the time of the model analysis are displayed, as well as strategies with staff turnover and increased resident and staff vaccination uptake.Given the lower baseline vaccine uptake among staff, increasing staff uptake makes the largest incremental difference in preventing both resident and staff infections

Fig. 3
Fig. 3 Factors affecting development of new infections among residents staff of homes individuals with serious mental illness and intellectual disability/developmental disability in Massachusetts.This figure displays the projected impact on the of varying uncertain input parameters, including vaccine efficacy and vaccination uptake among residents, staff, and community members, on the development of new COVID-19 infections among a residents and b staff of group homes for individuals with serious mental illness and intellectual/developmental disability in Massachusetts included in a hybrid implementation-effectiveness trial.The values of each parameter are indicated in parentheses in the following format: (smallest value examined -largest value examined; value in the base case Vaccination Current Levels strategy).The orange line in (a) indicates the number of projected resident infections (84) for the base case Vaccination Current Levels strategy.The orange line in (b) indicates the number of projected staff infections (519) for the base case Vaccination Current Levels strategy.The results summarized in the figure helped to inform targets of the trial's tailored best practice intervention package for COVID-19 risk mitigation

Table 1
Input parameters for an analysis of COVID-19 mitigation strategies in congregate living facilities for individuals with serious mental illness and intellectual/developmental disabilities in Massachusetts Pre-vaccination Status Quo: In addition to the measures included in the Early Pandemic with Masks prevention strategy, this prevention strategy also included the GH staff and resident symptom screening, asymptomatic testing among staff in GHs, isolation, and contact-time reflecting the standard practices in our GH population just prior to the introduction of vaccines in early 2021.4. Vaccination Current Levels: In addition to all measures included in the Pre-vaccination Status Quo strategy, this prevention strategy reflected the addition of vaccination uptake in 72% of residents and 58% of staff, reflecting population vaccination levels current during the analysis in early March 2021 after the initial wave of vaccinations in the GH study group.5. Vaccination Target Uptake: In addition to all measures included in the Vaccination Current Levels strategy, this prevention strategy reflected the target vaccination uptake of 90% both in the population of GH residents and staff in March 2021.

Table 2
Results of an analysis of initial management strategies over 90 days for COVID-19 prevention in group homes for individuals with serious mental illness and intellectual/ developmental disabilities in Massachusetts

Table 3
Results of a modeling analysis of vaccination strategies for COVID-19 prevention in group homes for individuals with serious mental illness and intellectual/developmental disabilities in Massachusetts